About this Course
최근 조회 99,373

다음 전문 분야의 3개 강좌 중 3번째 강좌:

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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 18시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

귀하가 습득할 기술

Python ProgrammingPrincipal Component Analysis (PCA)Projection MatrixMathematical Optimization

다음 전문 분야의 3개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 18시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 5시간 필요

Statistics of Datasets

8개 동영상 (총 27분), 6 readings, 4 quizzes
8개의 동영상
Welcome to module 141
Mean of a dataset4m
Variance of one-dimensional datasets4m
Variance of higher-dimensional datasets5m
Effect on the mean4m
Effect on the (co)variance3m
See you next module!27
6개의 읽기 자료
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Additional readings & helpful references5m
Set up Jupyter notebook environment offline10m
Symmetric, positive definite matrices10m
3개 연습문제
Mean of datasets15m
Variance of 1D datasets15m
Covariance matrix of a two-dimensional dataset15m
2
완료하는 데 4시간 필요

Inner Products

8개 동영상 (총 36분), 1 reading, 5 quizzes
8개의 동영상
Dot product4m
Inner product: definition5m
Inner product: length of vectors7m
Inner product: distances between vectors3m
Inner product: angles and orthogonality5m
Inner products of functions and random variables (optional)7m
Heading for the next module!35
1개의 읽기 자료
Basis vectors20m
4개 연습문제
Dot product10m
Properties of inner products20m
General inner products: lengths and distances20m
Angles between vectors using a non-standard inner product20m
3
완료하는 데 4시간 필요

Orthogonal Projections

6개 동영상 (총 25분), 1 reading, 3 quizzes
6개의 동영상
Projection onto 1D subspaces7m
Example: projection onto 1D subspaces3m
Projections onto higher-dimensional subspaces8m
Example: projection onto a 2D subspace3m
This was module 3!32
1개의 읽기 자료
Full derivation of the projection20m
2개 연습문제
Projection onto a 1-dimensional subspace25m
Project 3D data onto a 2D subspace40m
4
완료하는 데 5시간 필요

Principal Component Analysis

10개 동영상 (총 52분), 5 readings, 2 quizzes
10개의 동영상
Problem setting and PCA objective7m
Finding the coordinates of the projected data5m
Reformulation of the objective10m
Finding the basis vectors that span the principal subspace7m
Steps of PCA4m
PCA in high dimensions5m
Other interpretations of PCA (optional)7m
Summary of this module42
This was the course on PCA56
5개의 읽기 자료
Vector spaces20m
Orthogonal complements10m
Multivariate chain rule10m
Lagrange multipliers10m
Did you like the course? Let us know!10m
1개 연습문제
Chain rule practice20m
4.0
238개의 리뷰Chevron Right

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이 강좌를 수료한 후 새로운 경력 시작하기

50%

이 강좌를 통해 확실한 경력상 이점 얻기

Mathematics for Machine Learning: PCA의 최상위 리뷰

대학: JSJul 17th 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

대학: JVMay 1st 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

강사

Avatar

Marc Peter Deisenroth

Lecturer in Statistical Machine Learning
Department of Computing

임페리얼 칼리지 런던 정보

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

머신 러닝 수학 전문 분야 정보

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning....
머신 러닝 수학

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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